Ecological System Levels of Risk Factors for Intimate Partner Homicide Perpetration and Victimization: A Three-Level Meta-analysis

This study aims to identify, through an advanced three-level meta-analytic approach, the predictive validity of the different ecological systems for IPH perpetration and victimization relative to IPV, other homicides, IPH followed by suicide, and non-abused control groups. Twenty-nine studies were included, with 473 effect sizes in a unique population of 16,237. Each factor was classified into one of three ecological systems: ontogenetic, microsystem, and exosystem. The results revealed that the impact of each ecological system on the likelihood of IPH varies depending on the offender/victim gender and the comparison groups. Specifically, the largest odds ratios for intimate partner femicide (IPF) perpetration/victimization versus IPV were found at the microsystem level as well as IPF victimization versus non-abused group at the ontogenetic level of risk factors. Moderator analyses showed that the difference between male IPH and IPV perpetrators in the effect of the microsystem level of risk factors is explained in part by a set of cultural values and beliefs toward equality. We then discuss the present results' implications for risk assessment and the prevention/treatment of IPH perpetration and victimization, as well as future research.

Does it work? Using a Meta-Impact score to examine global effects in quasi-experimental intervention studies

The evaluation of applied psychological interventions in the workplace or elsewhere is challenging. Randomisation and matching are difficult to achieve and this often results in substantial heterogeneity within intervention and control groups. As a result, traditional comparison of group means using null hypothesis significance testing may mask effects experienced by some participants. Using longitudinal studies of coaching interventions designed to provide support for dyslexic employees, this study describes and evaluates a different approach using a Meta-Impact score. We offer a conceptual rationale for our method, illustrate how this score is calculated and analysed, and show how it highlights person-specific variations in how participants react and respond to interventions. We argue that Meta-Impact is an incremental supplement to traditional variable-centric group-wise comparisons and can more accurately demonstrate in practice the extent to which an intervention worked. Such methods are needed for applied research, where personalized intervention protocols may require impact analysis for policy, legal and ethical purposes, despite modest sample sizes.

It's the Theory, Stupid

To the complex question of “What is the number one issue on which we should focus as producers, evaluators, and consumers of research?” our simple and blunt answer is: It's the theory, stupid. Accordingly, we offer guidance on how to produce, test, and use theory by answering the following questions: (1) Why is theory so critical and for whom? (2) What does a good theory look like? (3) What does it mean to have too much or too many theories? (4) When don’t we need a theory? (5) How does falsification work with theory? and (6) Is good theory compatible with current publication pressures? Our answers are useful to current and future scholars and journal editors and reviewers, as well as consumers of research including other researchers, organization decision makers, and policy makers, and other stakeholders in the theory production and testing process including deans and other university administrators.

A Friend in Need Is a Friend Indeed? Analysis of the Willingness to Share Self-Produced Electricity During a Long-lasting Power Outage

Will private households owning a photovoltaic system share their electricity during a long-lasting power outage? Prior research has shown that our energy systems need to become more resilient by using dispersed energy sources-a role that could well be performed by these private photovoltaic systems, but only if their owners decide to share the produced electricity, and not consume it themselves. Considering the potential of this approach, it is indispensable to better understand incentives and motives that facilitate such cooperative behaviour. Drawing on theories of social dilemmas as well as prosocial behaviour, we hypothesize that both, structural solutions such as increased rewards as well as individual motives such as empathy-elicited altruism and norms predict cooperation. We test these hypotheses against a dataset of 80 households in Germany which were asked about their sharing behaviour towards four different recipient groups. We show that the effectiveness of motives differs significantly across recipient groups: Individual (intrinsic) motivations such as empathy-elicited altruism and altruistic norms serve as a strong predictor for cooperative behaviour towards related recipients as well as critical infrastructure, whereas higher rewards partially even reduce cooperation depending on the donor's social value orientation. For the recipient groups neighbours and public infrastructure, no significant effect for any of the tested incentives is found. Contributing to literature on social dilemmas and energy resilience, these results demonstrate the relevance of individual rather than structural incentives for electricity sharing during a power outage to render our energy provision more resilient. Practical implications for policymakers are given.

Does the Format of Preclass Reading Quizzes Matter? An Evaluation of Traditional and Gamified, Adaptive Preclass Reading Quizzes

Preclass reading quizzes (RQs) have been shown to enhance student performance. Many instructors implementing evidence-based teaching assign preclass RQs to ensure their students are prepared to engage in class activities. Textbook companies now offer a gamified, adaptive-learning RQ format. In these RQs, students answer point-valued questions until they reach a threshold. If students answer incorrectly, the question decreases in point value on the next attempt. These RQs also give students who answer questions incorrectly more questions on that topic and direct students to sections of a textbook they need to review. We assessed the impact of gamified, adaptive preclass RQs compared with more traditional preclass RQs on in-class RQs and course exam performance as well as students' perceptions of RQs. Students in the gamified, adaptive treatment performed equally compared with students in the traditional, static treatment on in-class RQs and course exams. While students in the gamified, adaptive treatment did have a more positive perception of preclass RQs, this factor explained less than 3% of the variation in RQ perception. Our findings suggest that instructors should verify that gamified, adaptive technologies impact student learning in their course before integrating them into their course and asking students to pay for them.

High-Stakes Testing Case Study: A Latent Variable Approach for Assessing Measurement and Prediction Invariance

The existence of differences in prediction systems involving test scores across demographic groups continues to be a thorny and unresolved scientific, professional, and societal concern. Our case study uses a two-stage least squares (2SLS) estimator to jointly assess measurement invariance and prediction invariance in high-stakes testing. So, we examined differences across groups based on latent as opposed to observed scores with data for 176 colleges and universities from The College Board. Results showed that evidence regarding measurement invariance was rejected for the SAT mathematics (SAT-M) subtest at the 0.01 level for 74.5% and 29.9% of cohorts for Black versus White and Hispanic versus White comparisons, respectively. Also, on average, Black students with the same standing on a common factor had observed SAT-M scores that were nearly a third of a standard deviation lower than for comparable Whites. We also found evidence that group differences in SAT-M measurement intercepts may partly explain the well-known finding of observed differences in prediction intercepts. Additionally, results provided evidence that nearly a quarter of the statistically significant observed intercept differences were not statistically significant at the 0.05 level once predictor measurement error was accounted for using the 2SLS procedure. Our joint measurement and prediction invariance approach based on latent scores opens the door to a new high-stakes testing research agenda whose goal is to not simply assess whether observed group-based differences exist and the size and direction of such differences. Rather, the goal of this research agenda is to assess the causal chain starting with underlying theoretical mechanisms (e.g., contextual factors, differences in latent predictor scores) that affect the size and direction of any observed differences.

From Cross-Cultural Economic Experiments to Experimental Indigenous Management Research – A Suggestion

This study provides an overview, categorization, and integration of what has been achieved in the niche of cross-culture experimental economics (CCEE) so far, aiming to inspire indigenous management researchers to extend their methodological toolbox by including experimental methods. As a result of the review, I find that most of the early studies lack depth and contextualization as well as detailed explanation aboutwhyhuman behavior differs. Hence, a better understanding about the influence of culture on economic decision-making is rather limited if it cannot be explained in more detail. In contrast, deep contextualization is a principle in indigenous management research (IMR). Both have so far not benefited from each other in the study of how culture affects human behavior, as both currently develop in parallel. Following the call for high-quality IMR (Tsui, 2004), this paper argues that an experimental methodology can make a contribution to IMR in the future by drawing on the strengths of both IMR (i.e., contextualization) and CCEE (i.e., methodology).

Doing good does not preclude doing well: corporate responsibility and financial performance

Purpose

This paper aims to propose and test a modified interpretation of long-standing issues on the corporate responsibility (CR)–corporate financial performance (CFP) relationship: companies involved in CR are in general no better and no worse in their level of financial performance than companies without such engagement because of the trade-off between benefit and cost at firm level and imbalance between supply and demand at industry (market) level.

Design/methodology/approach

The authors apply this frame to a data set with more than 12,000 observations over a 14-year period, using confidence intervals, as a useful and statistically valid approach for testing the null hypothesis.

Findings

The present study’s findings support neutrality between CR and CFP at the firm and industry levels, implying that a firm’s CR involvement neither penalizes nor improves its CFP.

Research limitations/implications

CR activities may provide windows of opportunity for companies but do not systematically improve financial performance.

Practical implications

“Doing good” is not a panacea for corporate achievement with respect to market-facing activities. For firms to succeed, instead, they need to create and implement their business cases and models by converting their involvement in CR activities into drivers for better outcomes because investments in CR practices do alone not guarantee improved financial performance.

Originality/value

The innovations in this study are twofold. Conceptually, this paper proposes a comprehensive approach for a neutral CR–CFP linkage. Empirically, it introduces a novel and appropriate method for testing neutrality. These will mark an important advance in the theoretical and empirical debates over CR and CFP.

Let’s Perform and Paint! The Role of Creative Mediums in Enhancing Management Research Representation

The limited reach of management research results in missed opportunities to support the decision-making processes of business professionals and policy makers. To strengthen the impact of management research and overcome barriers posed by text-heavy representation, we advocate for the use of creative mediums (e.g., collage, film, poetry) to showcase the product of an inquiry, either alone or as a supplement to traditional reporting. We provide a rationale for how these mediums trigger interest, foster a multisensory experience, convey complex meaning, and spark contemporary, inclusive dialogues. Each of the four rationales is discussed by showing an example of previous use, and explaining how the respective barrier to research representation is overcome. We finally offer recommendations for how management researchers can employ creative mediums to enhance the fertility of their work.

Effects of Weapons on Aggressive Thoughts, Angry Feelings, Hostile Appraisals, and Aggressive Behavior: A Meta-Analytic Review of the Weapons Effect Literature

A landmark 1967 study showed that simply seeing a gun can increase aggression—called the “weapons effect.” Since 1967, many other studies have attempted to replicate and explain the weapons effect. This meta-analysis integrates the findings of weapons effect studies conducted from 1967 to 2017 and uses the General Aggression Model (GAM) to explain the weapons effect. It includes 151 effect-size estimates from 78 independent studies involving 7,668 participants. As predicted by the GAM, our naïve meta-analytic results indicate that the mere presence of weapons increased aggressive thoughts, hostile appraisals, and aggression, suggesting a cognitive route from weapons to aggression. Weapons did not significantly increase angry feelings. Yet, a comprehensive sensitivity analysis indicated that not all naïve mean estimates were robust to the presence of publication bias. In general, these results suggest that the published literature tends to overestimate the weapons effect for some outcomes and moderators.

The Double-Edged Sword of Big Data in Organizational and Management Research

While many disciplines embrace the possibilities that Big Data present for advancing scholarship and practice, organizational and management research has yet to realize Big Data’s potential. In an effort to chart this newfound territory, we briefly describe the principal drivers and key characteristics of Big Data. We then review a broad range of opportunities and risks that are related to the Big Data paradigm, the data itself, and the associated analytical methods. For each, we provide research ideas and recommendations on how to embrace the potentials or address the concerns. Our assessment shows that Big Data, as a paradigm, can be a double- edged sword, capable of significantly advancing our field but also causing backlash if not utilized properly. Our review seeks to inform individual research practices as well as a broader policy agenda in order to advance organizational and management research as a scientifically rigorous and professionally relevant field.

Not Just How Much You Know: Interactional Effect of Cultural Knowledge and Metacognition on Creativity in a Global Context—ADDENDUM

In view of MOR's recent changes to editorial policies (Lewin et al., 2016), this addendum is created to provide greater data transparency and richer discussions of our findings and their implications.

Understanding “It Depends” in Organizational Research

The study of interaction effects is critical for creating, extending, and bounding theory in organizational research. Integrating and extending prior work, we present a taxonomy of two-way interaction effects that can guide organizational scholars toward clearer, more precise ways of developing theory, advancing hypotheses, and interpreting results. Specifically, we identify three primary interaction types, including strengthening, weakening, and reversing effects. In addition, we explore subcategories within these interaction types. Our review of articles published in leading management and applied psychology journals from 2009 to 2013 supports the generalizability of this framework. We offer specific recommendations for using this taxonomy to deliver more precise development, testing, and interpretation of interaction hypotheses.

A Road Well Traveled: The Past, Present, and Future Journey of Strategic Human Resource Management

This article provides an overview of the field of strategic human resource management (SHRM) by tracing its roots, describing its current state, and predicting its future directions. We discuss some past stages in the evolution of the field, including eras of conceptual models, empirical examinations, and empirical critiques. We then discuss the present state regarding theory, the human resources (HR) system–performance relationship, multilevel analyses, fit and flexibility, and international HR research. Finally, we propose that future research needs to be more rigorous, more multilevel, more global, more focused on human capital, more integrated with strategy, and more integrated with practice.

Multilevel Modeling: Research-Based Lessons for Substantive Researchers

Organizations are multilevel systems. Most organizational phenomena are multilevel in nature, and their understanding involves variables (e.g., antecedents and consequences) that reside at different levels. The investigation of these phenomena requires appropriate analytical methods: multilevel modeling. These techniques are becoming increasingly popular among organizational psychology and organizational behavior (OPOB) researchers. In this article we review the literature that has evaluated the performance of multilevel modeling techniques to test multilevel direct and indirect effects and cross-level interactions. We also provide guidelines for OPOB researchers about the appropriate use of these techniques, and we suggest ways these techniques can contribute to theoretical advancement and research development in OPOB.

Mixed Methods in the Organizational Sciences

A growing methodological trend is emerging from the recognition that integrating qualitative and quantitative methods in the same study, that is, employing a mixed methods approach, can provide the necessary empirical intricacy and rigor to match the complexities of organizational phenomena. The authors describe opportunities and challenges of mixed methods research in the organizational sciences, explain how the articles offered in this Feature Topic help to advance mixed methods in our field, and offer suggestions for future work that may create additional progress.

The Critique of Empirical Social Science: New Policies at Management and Organization Review

At the June 2016 meeting of the International Association for Chinese Management Research, MOR organized a symposium to discuss the mounting criticisms of empirical social science and subsequent changes, as part of ongoing discussions affecting journal reviewing policies. This article overviews the history of modern empirical social science as the foundation of management, organization, and strategy research and the criticism of social science research, which has reached the point that some critics refer to current publication norms as encouraging and enabling the publication of junk science. Most importantly, however, this article outlines MOR's strategy going forward and the new reviewing initiatives that MOR is implementing as of Volume 13 (2017).

An Assessment of the Magnitude of Effect Sizes

This study compiles information from more than 250 meta-analyses conducted over the past 30 years to assess the magnitude of reported effect sizes in the organizational behavior (OB)/human resources (HR) literatures. Our analysis revealed an average uncorrected effect of r = .227 and an average corrected effect of ρ = .278 ( SDρ = .140). Based on the distribution of effect sizes we report, Cohen’s effect size benchmarks are not appropriate for use in OB/HR research as they overestimate the actual breakpoints between small, medium, and large effects. We also assessed the average statistical power reported in meta-analytic conclusions and found substantial evidence that the majority of primary studies in the management literature are statistically underpowered. Finally, we investigated the impact of the file drawer problem in meta-analyses and our findings indicate that the file drawer problem is not a significant concern for meta-analysts. We conclude by discussing various implications of this study for OB/HR researchers.

The Validity of Conscientiousness Is Overestimated in the Prediction of Job Performance

Introduction

Sensitivity analyses refer to investigations of the degree to which the results of a meta-analysis remain stable when conditions of the data or the analysis change. To the extent that results remain stable, one can refer to them as robust. Sensitivity analyses are rarely conducted in the organizational science literature. Despite conscientiousness being a valued predictor in employment selection, sensitivity analyses have not been conducted with respect to meta-analytic estimates of the correlation (i.e., validity) between conscientiousness and job performance.

Methods

To address this deficiency, we reanalyzed the largest collection of conscientiousness validity data in the personnel selection literature and conducted a variety of sensitivity analyses.

Results

Publication bias analyses demonstrated that the validity of conscientiousness is moderately overestimated (by around 30%; a correlation difference of about .06). The misestimation of the validity appears to be due primarily to suppression of small effects sizes in the journal literature. These inflated validity estimates result in an overestimate of the dollar utility of personnel selection by millions of dollars and should be of considerable concern for organizations.

Conclusion

The fields of management and applied psychology seldom conduct sensitivity analyses. Through the use of sensitivity analyses, this paper documents that the existing literature overestimates the validity of conscientiousness in the prediction of job performance. Our data show that effect sizes from journal articles are largely responsible for this overestimation.

An Expanded Decision-Making Procedure for Examining Cross-Level Interaction Effects With Multilevel Modeling

Cross-level interaction effects lay at the heart of multilevel contingency and interactionism theories. Also, practitioners are particularly interested in such effects because they provide information on the contextual conditions and processes under which interventions focused on individuals (e.g., selection, leadership training, performance appraisal, and management) result in more or less positive outcomes. We derive a new intraclass correlation, ρβ, to assess the degree of lower-level outcome variance that is attributed to higher-level differences in slope coefficients. We provide analytical and empirical evidence that ρβ is an index of variance that differs from the traditional intraclass correlation ρα and use data from recently published articles to illustrate that ρα assesses differences across collectives and higher-level processes (e.g., teams, leadership styles, reward systems) but ignores the variance attributed to differences in lower-level relationships (e.g., individual level job satisfaction and individual level performance). Because ρα and ρβ provide information on two different sources of variability in the data structure (i.e., differences in means and differences in relationships, respectively), our results suggest that researchers contemplating the use of multilevel modeling, as well those who suspect nonindependence in their data structure, should expand the decision criteria for using multilevel approaches to include both types of intraclass correlations. To facilitate this process, we offer an illustrative data set and the icc beta R package for computing ρβ in single- and multiple-predictor situations and make them available through the Comprehensive R Archive Network (i.e., CRAN).

Explained Variance Measures for Multilevel Models

One challenge in using multilevel models is determining how to report the amount of explained variance. In multilevel models, explained variance can be reported for each level or for the total model. Existing measures have been based primarily on the reduction of variance components across models. However, these measures have not been reported consistently because they have some undesirable properties. The present study is one of the first to evaluate the accuracy of these measures using Monte Carlo simulations. In addition, a measure based on the full partitioning of variance in multilevel models was examined. With the exception of the Level 2 explained variance measure, all other measures performed well across our simulated conditions.

An Ounce of Prevention Is Worth a Pound of Cure: Improving Research Quality Before Data Collection

We rely on classic as well as recently published sources to offer a review of theory, research design, and measurement issues that should be considered prior to conducting any empirical study. First, we examine theory-related issues that should be addressed before research design and measurement considerations. Specifically, we discuss how to make meaningful theoretical progress including the use of inductive and deductive approaches, address an important issue, and conduct research with a practical end in mind. Second, we offer recommendations regarding research design, including how to address the low statistical power challenge, design studies that strengthen inferences about causal relationships, and use control variables appropriately. Finally, we address measurement issues. Specifically, we discuss how to improve the link between underlying constructs and their observable indicators. Our review offers a checklist for use by researchers to improve research quality prior to data collection and by journal editors and reviewers to evaluate the quality of submitted manuscripts.

Best-Practice Recommendations for Defining, Identifying, and Handling Outliers

The presence of outliers, which are data points that deviate markedly from others, is one of the most enduring and pervasive methodological challenges in organizational science research. We provide evidence that different ways of defining, identifying, and handling outliers alter substantive research conclusions. Then, we report results of a literature review of 46 methodological sources (i.e., journal articles, book chapters, and books) addressing the topic of outliers, as well as 232 organizational science journal articles mentioning issues about outliers. Our literature review uncovered (a) 14 unique and mutually exclusive outlier definitions, 39 outlier identification techniques, and 20 different ways of handling outliers; (b) inconsistencies in how outliers are defined, identified, and handled in various methodological sources; and (c) confusion and lack of transparency in how outliers are addressed by substantive researchers. We offer guidelines, including decision-making trees, that researchers can follow to define, identify, and handle error, interesting, and influential (i.e., model fit and prediction) outliers. Although our emphasis is on regression, structural equation modeling, and multilevel modeling, our general framework forms the basis for a research agenda regarding outliers in the context of other data-analytic approaches. Our recommendations can be used by authors as well as journal editors and reviewers to improve the consistency and transparency of practices regarding the treatment of outliers in organizational science research.